Forecasting fierce floods with transferable AI in data-scarce regions

Flooding is one of the most devastating natural disasters that affects communities globally. In early May 2023, catastrophic floods swept through Rwanda and the Democratic Republic of the Congo, leading to over 600 deaths and displacing thousands. As climate change is supercharging extreme rainfall worldwide, similar floods could further intensify. For developing regions with limited resources to build flood defenses, accurate flood forecasting with sufficient lead time is a cost-effective adaptation strategy to mitigate flood impacts. However, the lack of ground measurements in these regions hinders our understanding of flood-generating mechanisms, thereby limiting our ability to build better models for improved flood forecasts.

Two decades have passed since the International Association of Hydrological Sciences launched the Predictions in Ungauged Basins initiative in 2003. Despite advances in catchment hydrological processes, explosions of Earth observation data, and surges in computational power, the progress primarily benefits regions with dense gauge networks. Developing regions, which lack sufficient streamflow observations, continue to struggle with inaccurate and unreliable flood forecasts. There is potential to leverage the improved forecast performance from data-rich watersheds to enhance forecasts in data-scarce areas. However, this is challenging for traditional hydrological forecasts because the simplified conceptual representations of hydrological processes limit their transferability across watersheds with different features. Recent strides in deep learning offer new opportunities to address this. In The Innovation, Binlan Zhang and colleagues1 design a watershed-aware streamflow forecast model based on artificial intelligence (AI) to bridge the flood forecast gap in data-scarce developing regions, leveraging transfer learning techniques in AI. They show that AI-based models can capture watershed-dependent hydrological responses while learning complex rainfall-streamflow relationships. This capability opens doors to significant improvements in streamflow forecasts in data-scarce regions.